There is a huge literature around this topic (change/gain scores), and I think the best references come from the biomedical domain, e.g. 

> Senn, S (2007). *Statistical issues in
> drug development*. Wiley (chap. 7 pp.
> 96-112)

In biomedical research, interesting work has also been done in the study of [cross-over trials][1] (esp. in relation to *carry-over* effects, although I don't know how applicable it is to your study).

[From Gain Score t to ANCOVA F (and vice versa)][2], from Knapp & Schaffer, provides an interesting review of ANCOVA vs. t approach (the so-called Lord's Paradox). The simple analysis of change scores is not the recommended way for pre/post design according to Senn  in his article [Change from baseline and analysis of covariance revisited][3] (Stat. Med. 2006 25(24)). Moreover, using a mixed-effects model (e.g. to account for the correlation between the two time points) is not better because you really need to use the "pre" measurement as a covariate to increase precision (through adjustment).

I also like [Ten Difference Score Myths][4] from Edwards, although it focuses on difference scores in a different context; but here is an [annotated bibliography][5] on the analysis of pre-post change (unfortunately, it doesn't cover very recent work). Van Breukelen also compared ANOVA vs. ANCOVA in randomized and non-randomized setting, and his conclusions support the idea that ANCOVA is to be preferred, at least in randomized studies (which prevent from regression to the mean effect).


  [1]: http://en.wikipedia.org/wiki/Crossover_study
  [2]: http://pareonline.net/pdf/v14n6.pdf
  [3]: http://onlinelibrary.wiley.com/doi/10.1002/sim.2682/abstract
  [4]: http://public.kenan-flagler.unc.edu/faculty/edwardsj/Edwards01b.pdf
  [5]: http://www.ori.org/~keiths/bibliography/statistics-prepost.html